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Informatique et Mathématiques appliquées
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Kernel methods for machine learning - WMM9MO14

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  • Number of hours

    • Lectures : 18.0
    • Tutorials : -
    • Laboratory works : -
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 3.0
  • Officials : Julien MAIRAL

Goals

Introduction to statistical learning theory and kernel-based methods.
Applications in bioinformatics, computer vision, text mining, audio processing, etc.

Content

I. Introduction

I.1. Statistical learning: issues and goals
I.2. Risk convexification and capacity control
I.3. Convex optimization for statistical learning
I.4 Real applications

II. Kernel-based methods

II.1. Similarity measures and reproducing kernels
II.2. Reproducing kernel Hilbert spaces
II.4. Main families of reproducing kernels
II.3. Regularization as spectral function

III. Supervised statistical learning

III.1. Kernel Ridge Regression
III.2. Kernel Logistic Regression
III.3. Support Vector Machine
III.4. Capacity control and risk bounds

IV. Unsupervised statistical learning

II.1. Kernel Principal Component Analysis
II.2. Kernel Canonical Correlation Analysis
II.3. Spectral clustering
II.4. Large margin clustering
III.4. Capacity control and risk bounds

Prerequisites

Probability, statistics, linear algebra.

Tests

un data challenge et un examen

The exam is given in english only FR

Calendar

The course exists in the following branches:

  • Curriculum - Master 2 in Applied Mathematics - Semester 9 (this course is given in english only EN)
  • Curriculum - Master 2 in Computer Science - Semester 9 (this course is given in english only EN)
see the course schedule for 2020-2021

Additional Information

Course ID : WMM9MO14
Course language(s): FR

You can find this course among all other courses.

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Date of update January 15, 2017

Université Grenoble Alpes